SCH: INT: Novel Techniques for Patient-centric Disease Management using Automatically Inferred Behavioral Biomarkers and Sensor-Supported Contextual Self-Report

只有当个体？S自我护理和临床决策由该个体？S健康状况的丰富预测模型提供信息时，以患者为中心、个性化、精准医疗和健康的愿景才能充分实现。移动技术的发展和传播创造了前所未有的机会，以更细粒度、更低调、甚至更实惠的方式收集高度详细和个性化的数据；这些数据包括活动水平、位置模式、睡眠、消费以及通信和社交。然而，要将这种潜力转化为实践，我们需要开发算法和方法，将这些原始数据转化为可操作的信息。这项研究将开发新的和可推广的技术，以得出与个人健康和临床决策相关的稳健措施。该团队将开发和评估将原始人类活动数据转换为临床可操作的行为生物标记物的工具。这需要创造性地使用潜在的技术能力(即被动数据捕获、数据分析和机器学习、数据可视化、用户体验)，并严格了解潜在的健康状况和管理(即功能性健康衡量标准、可实现和最佳的健康结果、患者在坚持治疗方面的挑战、与药物和治疗其他方面相关的风险和好处，以及临床决策)。该方法广泛适用于疾病管理(例如，自身免疫、胃肠、抑郁症、认知衰退和神经疾病)，但也要求针对特定条件和个人进行量身定做。因此，我们将在特定的背景下进行这项初步工作，即针对三种突出情况的慢性疼痛管理：类风湿性关节炎、骨关节炎和下腰痛。与我们最初的目标领域疼痛管理相关的行为生物标记物围绕：(I)活动水平下降；(Ii)压力增加；(Iii)睡眠质量下降；(Iv)功能下降，例如，旅行距离缩短或无法上班。手机的被动感知功能可以跟踪睡眠、活动水平的变化、压力、社交隔离、地理位置和其他几个可能是疼痛干扰的先兆或症状的指标，这一点以前已经证明过。虽然行为生物标记物广泛依赖于被动捕获的数据流(如活动、位置、通信、应用程序使用和音频)，但在一些重要情况下，需要自我报告数据来补充或澄清被动收集的数据。然而，由于篇幅、问题设计或两者兼而有之，评估相关症状和行为的标准化患者调查工具不适合日常使用。此外，传统的自我报告形式往往具有侵扰性、繁重和高流失率。一种新的方法，情景回忆，旨在通过三个关键机制来缓解与自我报告相关的问题：优化提示的传递，向用户提供关键的上下文线索以提高回忆能力，以及使用视觉输入技术作为不能很好地扩展到频繁移动自我报告的长期措施的替代方案。在可负担性和可获得性方面，个性化疾病管理的方法是有意可扩展的。被动数据收集不需要用户注意，情境回忆是为忙碌的个人设计的一种自我报告形式，这些人对自己的时间有一系列要求和限制，以及潜在的识字和算术限制。这种方法的面向临床医生的部分也被设计成在资源有限的临床环境中工作，其中临床医生处于特别的时间压力之下。该团队将从通常服务不足的社区招募患者和临床医生参与参与式设计过程。这项工作的总体贡献将包括以下方面的开发和评估：(1)软件技术，将被动监控和自我报告的数据流组合并转换为临床上有意义的、可操作的和个性化的指标，我们称之为行为生物标记物；(2)情境回忆，允许收集高度细粒度和上下文特定的自我报告数据，以增强被动捕获的数据与患者角度的信息，同时平衡在平衡回忆偏见和可用性方面所面临的紧张；以及(3)将与临床领域专家的合作系统化，以开发行为生物标记物并将其集成到特定疾病的临床决策中。我们将创建和评估一套模块化和可扩展的分析和用户交互技术，旨在促进迭代实施和评估。这些模块本身将是一项贡献，但同样重要的是对行为生物标记物作为精准医学驱动力的整体方法的评估。

The vision of patient-centric, personalized, precision medicine and wellness will be fully realized only when an individual?s self-care and clinical decision making are informed by a rich, predictive model of that individual?s health status. The evolution and dissemination of mobile technology has created unprecedented opportunities for highly detailed and personalized data collection in a far more granular, unobtrusive, and even affordable way; these data include activity levels, location patterns, sleep, consumption, and communication and social interaction. However, turning this potential into practice requires that we develop the algorithms and methodologies to transform these raw data into actionable information. The research will develop novel and generalizable techniques to derive robust measures relevant to individual health and clinical decision making. The team will develop and evaluate tools that convert raw human-activity data into clinically actionable behavioral biomarkers. This demands creative uses of the underlying technical capabilities (i.e., passive data capture, data analysis and machine learning, data visualization, user experience), as well as rigorous understanding of the underlying health condition and management (i.e. functional health measures, achievable and optimal health outcomes, patient challenges in adherence, risks and benefits associated with medication and other aspects of treatment, and clinical decision making). The approach has broad applicability across disease management (e.g., auto-immune, gastrointestinal, depression, cognitive decline, and neurologic disorders), but also calls for tailoring to specific conditions and individuals. Therefore, we will conduct this initial work in a specific context, that of chronic pain management for three prominent conditions: rheumatoid arthritis, osteoarthritis, and lower back pain. The behavioral biomarkers associated with our initial target domain, pain management, center around: (i) decline in activity levels; (ii) increase in stress; (iii) decrease in sleep quality; (iv) drop in function, e.g., reduction in travel distance or inability to go to work. The effectiveness of passive sensing capabilities of the mobile phone to track sleep, changes in activity level, stress, social isolation, geographic location and several other indicators that are likely antecedents or symptoms of pain interference has been demonstrated previously. While behavioral biomarkers rely extensively on passively captured data streams (such as activity, location, communication, application usage and audio), there remain important cases in which self-report data is required to augment or clarify passively collected data. However, the standardized patient survey instruments that assess relevant symptoms and behavior are not suitable for use on a daily basis because of length, question design, or both. Further, traditional forms of self report are often intrusive, burdensome, and suffer high rates of attrition. A new approach, contextual recall, aims to mitigate the issues related to self-report through three key mechanisms: optimizing the delivery of prompts, providing the user with key contextual cues to improve recall, and employing visual input techniques as an alternative to long-form measures that do not scale well to frequent mobile self-reports. The approach to personalizing disease management is intentionally scalable in terms of affordability and accessibility. Passive data collection requires no user attention, and contextual recall is a form of self-report designed for busy individuals with a range of demands and constraints on their time, as well as potential literacy and numeracy constraints. The clinician-facing components of this approach are also designed to work in resource-constrained clinical settings where clinicians are under particular time pressure. The team will recruit patients and clinicians from typically underserved communities to engage in the participatory design process. The overall contributions of this work will include development and evaluation of: (1) software techniques to combine and transform passively monitored and self-reported data streams into clinically meaningful, actionable, and personalized indicators, which we call behavioral biomarkers; (2) contextual recall that allows the collection of highly granular and contextually specific self-report data to enhance passively captured data with information from the patient perspective, while balancing the tension faced in balancing recall bias and usability; and (3) a methodology that systematizes the collaboration with clinical domain experts to develop and integrate behavioral biomarkers into clinical decision making for specific diseases. We will create and evaluate a modular and extensible suite of analytics and user interaction techniques designed to facilitate iterative implementation and evaluation. These modules will themselves be a contribution, but equally important will be the evaluation of the overall approach of behavioral biomarkers as a driver of precision medicine.